​
Code

cookbook/models/aws/bedrock/storage.py
from agno.agent import Agent
from agno.models.aws import AwsBedrock
from agno.storage.agent.postgres import PostgresAgentStorage
from agno.tools.duckduckgo import DuckDuckGoTools

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

agent = Agent(
    model=AwsBedrock(id="mistral.mistral-large-2402-v1:0"),
    storage=PostgresAgentStorage(table_name="agent_sessions", db_url=db_url),
    tools=[DuckDuckGoTools()],
    add_history_to_messages=True,
)
agent.print_response("How many people live in Canada?")
agent.print_response("What is their national anthem called?")

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.

python3 -m venv .venv
source .venv/bin/activate
2

Set your AWS Credentials

export AWS_ACCESS_KEY_ID=***
export AWS_SECRET_ACCESS_KEY=***
export AWS_REGION=***
3

Install libraries

pip install -U boto3 duckduckgo-search sqlalchemy psycopg agno
4

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:16
5

Run Agent

python cookbook/models/aws/bedrock/storage.py
Agent with Structured OutputsAgent with Knowledge